Modification of device settings based on user abilities

ABSTRACT

Provided is a method, computer program product, and system for modifying IoT device settings based on changes in user abilities. A processor may detect a plurality of IoT devices associated with one or more users. The processor may determine an ability baseline for the one or more users when interacting with the plurality of IoT devices. The processor may monitor interactions of the one or more users with the plurality of IoT devices. The processor may detect a change in the ability baseline for the one or more users when interacting with at least one IoT device of the plurality of IoT devices. In response to detecting the change, the processor may adjust one or more settings related to the at least one IoT device. The processor may propagate the adjustment to the one or more settings of the IoT device to the other IoT devices of the plurality.

BACKGROUND

The present disclosure relates generally to the field of Internet ofThings (IoT) devices, and more specifically, to utilizing a centralizedsystem to automatically modify IoT device settings based on detectedchanges in user abilities.

IoT devices have become ubiquitous in everyday life. IoT devices can befound in nearly every environment (e.g., homes, businesses, etc.). Forexample, many homes utilize various IoT devices such as smart speakers,smart televisions, smart thermostats, and even smart refrigerators thatare connected wirelessly to the internet.

SUMMARY

Embodiments of the present disclosure include a method, computer programproduct, and system for modifying IoT device settings based on changesin user abilities. A processor may detect a plurality of IoT devicesassociated with one or more users. The processor may determine anability baseline for the one or more users when interacting with theplurality of IoT devices. The processor may monitor interactions of theone or more users with the plurality of IoT devices. The processor maydetect a change in the ability baseline for the one or more users wheninteracting with at least one IoT device of the plurality of IoTdevices. The processor may adjust, in response to detecting the changein the ability baseline for the one or more users, one or more settingsrelated to the at least one IoT device. The processor may propagate theadjustment to the one or more settings of the at least one IoT device tothe other IoT devices of the plurality.

Embodiments of the present disclosure include a method, computer programproduct, and system for modifying IoT device settings based on changesin user abilities. A processor may determine an ability baseline for auser. The processor may monitor interactions of the user with thesystem. The processor may detect a change in the ability baseline forthe user based on the monitoring the interactions of the user. Theprocessor may adjust one or more settings related to the system inresponse to detecting the change in the ability baseline for the user.The processor may transmit the adjustment to the one or more settings toat least one IoT device associated with the user.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present disclosure are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative oftypical embodiments and do not limit the disclosure.

FIG. 1 illustrates a block diagram of an example system architecture, inaccordance with embodiments of the present disclosure.

FIG. 2 illustrates a flow diagram of an example process for adjustingone or more settings of an IoT device based on change in abilitybaseline, in accordance with embodiments of the present disclosure.

FIG. 3 illustrates a flow diagram of an example process for adjustingability baseline of a user based on a cohort, in accordance withembodiments of the present disclosure.

FIG. 4 illustrates a flow diagram of an example process for modifyingone or more settings of an IoT device using a rule determination, inaccordance with embodiments of the present disclosure.

FIG. 5 illustrates a high-level block diagram of an example computersystem that may be used in implementing one or more of the methods,tools, and modules, and any related functions, described herein, inaccordance with embodiments of the present disclosure.

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to the field of Internet ofThings (TOT) devices, and more particularly to utilizing a centralizedsystem to modify IoT device settings based on detected changes in userabilities. While the present disclosure is not necessarily limited tosuch applications, various aspects of the disclosure may be appreciatedthrough a discussion of various examples using this context.

IoT devices have become ubiquitous in everyday life. IoT devices can befound in nearly every environment (e.g., homes, businesses, etc.). Forexample, many homes utilize various IoT devices such as smart speakers,smart televisions, smart thermostats, and even smart refrigerators thatare connected wirelessly to the internet. Typically, each of the IoTdevices has various settings for controlling specific aspects of thedevice.

For example, some IoT devices may include a user interface (UI) havingsettings that may be altered by a user to suit their preferences. Insome instances, a user may prefer larger font size or a lighter ordarker contrast when viewing the UI. Some IoT devices may include othersettings, such as alarm or sound settings, where the volume may beadjusted to a preferred level by the user. Typically, each IoT devicehas settings that must be adjusted by the user manually. Over time, theuser may have to readjust settings to suit their needs. For example, auser may need to adjust font settings on their smart phone to a largersize if the user's vision has changed. In such an instance, the user mayhave to similarly adjust font settings for all other IoT devices thatutilize text to accommodate their change in vision.

Aspects of the present disclosure relate to automatically adjustingsettings of a plurality of IoT devices, collectively, based ondetermining changes in user abilities via a centralized system. Inembodiments, a centralized system (e.g., host device) may detect aplurality of IoT devices that are associated with one or more users. Forexample, the system may detect various IoT devices (e.g., smart homedevices and/or appliances) throughout an environment (e.g., home,business, etc.) that are wirelessly connected to the centralized system.The centralized system may be any type of device (e.g., wireless router,smart speaker, computer, etc.) that connects to the IoT devices.

The system may determine an ability baseline for one or more users whenthe users interact with the IoT devices. The determination of theability baseline of each user may be performed in various ways. In someembodiments, the initial ability baseline is determined by the system byanalyzing user preferences associated with the one or more users (e.g.,via user profile preferences) for each respective IoT device. Forexample, the system may use current preference settings (e.g., volume,contrast, font size, alarm settings, etc.) on the IoT devices (e.g.,smartphone, laptop/computer, internet browser/application, television(TV) volume, etc.) to determine the ability baseline. In someembodiments, the system may utilize various response metrics todetermine the user's ability baseline. For example, the system maydetermine the average time a user takes to respond to a beepingalarm/notification of an IoT device (e.g., oven, microwave, etc.) anduse this to determine the initial ability baseline of the user wheninteracting with the specific IoT device. In this way, the system cantrack how the user initially interacts with each IoT device. In someembodiments, the user's ability baseline may encompass a range of valuesas determined by the system. For example, the system may determine thatthe user prefers font sizes between 12 and 14 size font based oncomparing font preferences from different IoT devices. In someembodiments, the range may include a decibel range based on varyingvolume preferences when the user interacts with IoT devices that emitsound.

In some embodiments, the ability baseline may be determined using aself-guided survey which presents different options (e.g., fonts, fontsizes, sound levels, mobility options, background contrasts, etc.) forthe user to select. In embodiments, the survey may further ask if theuser is currently using any compensatory systems (e.g., hearing devices,glasses, etc.) that may affect the user's ability baseline. Inembodiments, the survey may further utilize ecosystem data or profiledata (e.g. social media profiles, pictures, etc.) to assess via imageanalysis if the individual utilizes any compensatory systems.

In some embodiments, the ability baseline may be determined by afeedback loop. For example, the system may simulate current IoTdevice/appliance settings to aid the user in selecting the appropriatesettings for their needs via feedback loop. For example, the system maypresent decibel settings (e.g., ring tone option) and font sizesassociated with a smart phone for the user to select according to theirpreferences. In some embodiments, the ability baseline for the one ormore users may be determined by gamification. For example, the systemmay apply typical elements of game playing (e.g., point scoring,competition with others, rules of play) to determine the user's abilitybaseline. For example, the system may use game play to determine theuser's reaction ability in response to various gaming stimuli.

Once the ability baseline is established for the one or more users, thesystem may monitor interactions of the one or more users with theplurality of IoT devices. For example, the system may monitor which userinteracts with each IoT device, how often they interact with each IoTdevice, and if the user's ability baseline when interacting with eachIoT device has changed over a certain time period. For example, thesystem may track the user's speed of action in response to IoTdevice/appliance alerts with respect to the individual's proximity tothe IoT device/appliance. For example, the user may be in a kitchen whenan oven timer goes off. The system may monitor the user with respect tothe oven timer alarm to evaluate if there is any change in interactionwith the device. If the system detects an increase in time differencefor the user when addressing the alarm (e.g., indicating the user maynot have heard the alarm because the volume is too low), the system maydetermine that there is a change in the user's ability baseline (e.g.slower reaction time or no reaction to the alarm).

Once the system detects a change in the ability baseline of the user,the system will automatically adjust one or more settings related to therespective IoT device. For example, if the system determines the user isnot responding to the oven alarm, the system may automatically adjustthe volume of the oven alarm in order for the user to hear it.

Once the system adjusts the settings of the respective IoT device, thesystem may propagate similar adjustment of settings (if applicable) forother IoT devices linked to the system. For example, the system mayincrease all volume settings on other IoT devices that have alarmssimilar to the oven alarm. In another example, the system may determinethe user is having difficulty reading text on a UI of an IoT device(e.g., based on slower interaction data when choosing options and/orselection of incorrect options on the UI, image recognition, etc.). Thesystem may determine the user's ability baseline has changed andautomatically update UI settings. For example, the system may correlatechanges in the user's ability baseline for reading text andautomatically increase font size settings on the IoT device to aid theuser when utilizing the UI. Once adjusted, the system may propagate themodification of the font settings to all UIs of other applicable IoTdevices accordingly. In this way, the user does not have to adjust fontsettings on other applicable devices, rather the system willautomatically perform the adjustment.

In embodiments, the system may analyze historical ability baseline datafor a plurality of users in order to predict cohort needs and preferencesettings for successful interactions with IoT devices/appliances. Forexample, the system may collect historical ability baseline data for aplurality of users. The system may correlate the historical abilitybaseline data with adjustments of settings for a plurality of IoTdevices in relation to the plurality of users. Once correlated, thesystem may group the plurality of users into one or more cohorts basedon similar adjustment of the settings. For example, the system may groupusers having similar vision capabilities in a specific cohort based onsuccessful adjustment of font settings. Once the cohort is established,font settings for new users placed in the cohort will automatically beupdated accordingly.

In embodiments, the system may distinguish between users within anenvironment and apply appropriate settings for each applicable IoTdevice. For example, the system may detect that two or more users havingdifferent ability baselines are attempting to use one or more IoTdevices concurrently. In response, the system may determine whichability baseline to apply using a rule determination. The rule may bebased on preferential ranking or scoring. For example, the owner of thesystem, or head of the household may outrank one or more other userssuch that the system will apply the highest ranked settings to the IoTdevices.

In some embodiments, the adjustment of the settings of an IoT device maybe transmitted to other applicable IoT devices without the use of acentralized system (e.g., peer-to-peer, ad-hoc network, etc.). Forexample, rather than using a centralized system, the IoT device maymonitor the interactions of the user with the IoT device itself, detectchanges in the ability baseline of the user based on the interactions ofthe user, and adjust settings related to the IoT device based on thedetected change in the ability baseline of the user. Once adjustment tothe settings are made on the IoT device, the IoT device may transmit theadjustment to settings to other applicable IoT devices, such thatsimilar adjustments to the setting of the other applicable IoT devicescan be made.

The aforementioned advantages are example advantages, and not alladvantages are discussed. Furthermore, embodiments of the presentdisclosure can exist that contain all, some, or none of theaforementioned advantages while remaining within the spirit and scope ofthe present disclosure.

With reference now to FIG. 1, shown is a block diagram of an examplesystem architecture 100, in accordance with embodiments of the presentdisclosure. In the illustrated embodiment, system architecture 100includes host device 102 that is communicatively coupled to IoT device120A, IoT device 120B, IoT device 120N (collectively referred to as IoTdevices 120), and server 130 via network 150. In embodiments, IoTdevices 120 may be any type of device (e.g., computer, smartphone,tablet, speaker, television, dishwasher, refrigerator, coffee maker,smart light, hearing aid, etc.) configured to communicatively connect tohost device 102.

IoT devices 120 and host device 102 may be any type of computer systemand may be substantially similar to computer system 1101 of FIG. 5. Forexample, host device 102 may be a wireless router that acts as acentralized system that communicatively couples to IoT devices 120. Inanother example, host device 102 may be a smart speaker that iscommunicatively coupled to IoT devices 120.

Network 150 may be any type of communication network, such as a wirelessnetwork or a cloud computing network. The network 150 may besubstantially similar to, or the same as, cloud computing environment 50described in FIG. 6. In some embodiments, the network 150 can beimplemented using any number of any suitable communications media. Forexample, the network may be a wide area network (WAN), a local areanetwork (LAN), an internet, or an intranet. In certain embodiments, thevarious systems may be local to each other, and communicate via anyappropriate local communication medium.

For example, host device 102 may communicate with IoT devices 120 and/orserver 130 using a WAN, one or more hardwire connections (e.g., anEthernet cable) and/or wireless communication networks. In someembodiments, the various systems may be communicatively coupled using acombination of one or more networks and/or one or more localconnections. For example, host device 102 may communicate with server130 using a hardwired connection, while communication between the hostdevice 102 and IoT devices 120 may be through a wireless communicationnetwork.

In the illustrated embodiment, host device 102 includes processor 104,ability baseline module 106, machine learning module 108, and database110. In embodiments, host device 102 may utilize database 110 to storevarious user profile data and/or user preference settings pertaining toeach IoT device 120. In embodiments, only user profile data pertinent toIoT devices 120 settings will be shared via an application programinginterface (API) call in order to change IoT device 120 settingsaccording to the user's preferences (e.g. increasing volume to a certaindecibel, modifying fonts, adjusting contrast, limiting distractions,etc.). In embodiments, user profile data may be stored on host device102 and/or server 130 (e.g., cloud server) and accessed using a uniquekey or portable device to maintain security. In embodiments, each time auser accesses system 100 or utilizes any connected IoT devices 120, thehost device 102 may analyze any new user profile data and updatedatabase 110.

In the illustrated embodiment, IoT device 120A includes settings 122A,IoT device 120B includes settings 122B, and IoT device 120N includessettings 122N. The settings 122A, 122B, and 122N (collectively referredto as settings 122) may include various parameters that may be manuallyset by the user or automatically set by host device 102. In embodiments,IoT devices 120 are registered with host device 102 such that hostdevice 120 can control settings 122 via API calls. In some embodiments,IoT devices 120 may include a user interface (UI). The UI may presentsettings 122 for adjusting: fonts on the display, color contrasts of thedisplay, decibels of the volume for alerts, notifications (e.g., firealarm, microwave beeping, etc.), and the like. In some embodiments,settings 122 for IoT devices 120 may include activating assistivetechnology (e.g., closed captioning on a television, adjusting lightingto eye sensitivity, or increasing brightness for reading when using attablet, etc.).

In embodiments, IoT devices 120 may be any type of smart device. Forexample, IoT devices 120 may be configured as smart speakers, andsettings 122 may be adjusted according to a user's ability baseline(e.g., a user with difficulty hearing) such that sound is localized tospecific speakers which are in closest proximity to the user. In someembodiments, IoT devices 120 may be configured as wearable devices(e.g., cochlear implant, smartwatch, etc.) where settings are augmentedbased on user's ability baseline (e.g., increasing alert volume, etc.).

In embodiments, ability baseline module 106 is configured to determinethe ability baseline for each user associated with IoT devices 120. Inembodiments, ability baseline module 106 may detect settings 122 anddetermine the ability baseline for each user using these settings. Forexample, the ability baseline module 106 may track volume settings ofall IoT devices 120 (e.g., smart televisions, alarms, microwaves,washers, dryers, speakers, etc.) and determine the user's abilitybaseline for hearing sounds using these settings. Once an initialability baseline is determined for each user, ability baseline module106 monitors and detects any changes in the user's ability baseline wheninteracting with each of the IoT device 120 over time.

For example, ability baseline module 106 may analyze voice commands of auser when interacting with IoT device 120A (e.g., a smart speaker) thatrequest for volume settings 122A to be turned up over time. Abilitybaseline module 106 will detect these changes in volume settings 122Afor IoT device 120A and determine there has been a change in the user'sability baseline regarding volume levels. Once determined, abilitybaseline module 106 will automatically update volume settings 122B and122N in IoT device 120B (e.g., a smart television) and IoT device 120N(e.g., a smart phone), respectively.

In another example, ability baseline module 106 may detect theactivation of closed captions on IoT device 120B (e.g., the smarttelevision) and apply the changes to only IoT devices 120 that includeapplicable settings. For example, closed captions may be automaticallyenabled on a smart phone but not a smart speaker since there may be noaccompanying graphical UI. In another example, ability baseline module106 may detect changes in color/contrast and/or text size on an IoTdevice 120 such as a smart phone or tablet. This may indicate a changein the user's vision. Ability baseline module 106 will note the changesin the user's ability baseline and adjust the appropriate settings 122(e.g., color/contrast, text size) of each applicable IoT device 120.

In embodiments, machine learning module 108 may comprise various machinelearning engines (artificial neural network, correlation engines,reinforcement feedback learning model, supervised/unsupervised learningmodel, etc.) configured to analyze data generated from the system 100.For example, machine learning module 108 may analyze historical userprofile data and/or historical user ability baseline data generated fromthe system 100 and correlate changes in settings over time for varioususers in order to group users into cohorts. Machine learning module 108may group users based on similar digital environments, abilities, IoTdevice settings, and characteristics (e.g., demographic information,physical/cognitive ability, location, cultural information, digitalenvironments, types of IoT devices, number of inhabitants, etc.).Machine learning module 108 may use the cohorts to predict preferencesfor other users based on settings for successful interactions with IoTdevices/appliances. For example, machine learning module 108 may useunsupervised learning algorithms to group user's into specific cohorts,derive properties from the cohorts, and relate the properties with otheror new users in order to predict the appropriate settings for successfulinteraction with each IoT device 120. In embodiments, machine learningmodule 108 may utilize a feedback learning model to collect initial usersetting preferences and ability baselines for interacting withrespective IoT devices to reinforce the supervised and unsupervisedmodels. Over time, host device 102 can become more accurate in properlyadjusting user settings on IoT devices 120 according to the user'schanges in ability baseline.

FIG. 1 is intended to depict representative components of the examplesystem architecture 100. In some embodiments, however, individualcomponents may have greater or lesser complexity than as represented inFIG. 1, components other than or in addition to those shown in FIG. 1may be present, and the number, type, and configuration of suchcomponents may vary. Likewise, one or more components shown with theexample system architecture 100 may not be present, and the arrangementof components may vary.

For example, while FIG. 1 illustrates an example system architecture 100having a single host device 102, three IoT devices 120, and a singleserver 130, suitable network architectures for implementing embodimentsof this disclosure may include any number of host devices, IoT devices,and servers. The various models, modules, systems, and componentsillustrated in FIG. 1 may exist, if at all, across a plurality of hostdevices, IoT devices, and servers.

Referring now to FIG. 2, shown is a flow diagram of an example process200 for adjusting one or more settings of an IoT device based on changein ability baseline, in accordance with embodiments of the presentdisclosure. The process 200 may be performed by processing logic thatcomprises hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on aprocessor), firmware, or a combination thereof. In some embodiments, theprocess 200 is a computer-implemented process. The process 200 may beperformed by processor 104 exemplified in FIG. 1.

The process 200 begins by detecting a plurality of IoT devices. This isillustrated at step 205. The plurality of IoT devices 120 are associatedwith one or more users and communicatively coupled to host device 102.For example, host device 102 may be a wireless router that iscommunicative coupled to various smart devices and appliances (e.g.,smart speakers, smart televisions, smart watches, smart dishwashers,smart ovens, and the like) throughout a digital environment.

The process 200 continues by determining an ability baseline for the oneor more users when interacting with the plurality of IoT devices. Thisis illustrated at step 210. In some embodiments, the initial abilitybaseline is determined by analyzing user preferences associated with theone or more users for each respective IoT device. For example, thesystem may use current preference settings (e.g., volume, contrast, fontsize, alarm settings, etc.) on the IoT devices to determine the abilitybaseline. In some embodiments, the system may utilize various responsemetrics to determine the user's ability baseline. In some embodiments,the ability baseline may be determined using a self-guided survey whichpresents different options (e.g., fonts, font sizes, sound levels,mobility options, background contrasts, etc.) for the user to select. Inembodiments, the survey may further ask if the user is currently usingany compensatory systems (e.g., hearing devices, glasses, etc.). In someembodiments, the ability baseline may be determined by a feedback loopand/or gamification.

In some embodiments, once the ability baseline is determined, theinitial preferences may be propagated accordingly to all applicable IoTdevices connected to the system. By centralizing this process, the useris tasked to perform a minimum number of human-computer interactions.

The process 200 continues by monitoring interactions of the one or moreusers with the plurality of IoT devices. This is illustrated at step215. For example, the system may monitor actions of the user withrespect to each of the plurality of IoT devices. For example, the systemmay monitor how often the user changes settings on the IoT devices. Forexample, the system may monitor if the user has increased volumesettings on one or more IoT devices. In some embodiments, the system mayuse various communicatively coupled IoT devices (e.g., sensors, cameras,etc.) to monitor user interactions with other IoT devices. For example,the system may use an IoT camera to determine a user is havingdifficulty reading text on the UI of an IoT device (e.g., imagerecognition indicating the user is holding the screen close to theirface when reading).

The process 200 continues by detecting a change in the ability baselinefor the one or more users when interacting with at least one IoT deviceof the plurality of IoT devices. This is illustrated at step 220. Forexample, a change in ability baseline may be detected if the user turnsup the volume of the television over time. In another example, thesystem may determine that the user does not hear an alarm notificationfrom an IoT device (e.g., dishwasher, microwave, smoke alarm, etc.)because the user did not acknowledge the alarm or was slower than normalwhen acknowledging the alarm, which may indicate a change in hearingability baseline.

Once the system detects a change in the ability baseline of the one ormore users, the process 200 continues by adjusting one or more settingsfor the at least one IoT device based on the change. This is illustratedat step 225. For example, the system may detect the user is havingdifficulty reading text on the screen of their tablet via imagerecognition and/or speed of interaction with the UI. The system maydetect this change in ability baseline in relation to the user'seyesight and automatically increase the font size on the user's tablet.

Once the settings are adjusted on the at least one IoT device, theprocess 200 continues by propagating the adjustment to the one or moresettings of the IoT device to the other IoT devices of the plurality.This is illustrated at step 230. Returning to the previous example, themodification of font size on the user's tablet would be propagated toany other applicable IoT devices (e.g., smart phone, computer, smartappliances throughout environment etc.) that include fonts or text on aUI.

In embodiments, the adjustments/changes to various settings may be basedon the individual's proximity to the respective IoT device and/or thebase level ambient noises in the room. For example, for a microwave, ifthe user is in another room while the microwave alarm is beeping, thesystem may automatically increase the volume level of the alarm asopposed to if the user was near the device in the kitchen. In anotherexample, the system may detect background noise from a television, whichmay affect the user's ability baseline to hear the microwave alarm. Inturn, the system may increase the volume of the alarm to compensate forthe background television noise.

In another example, the system may modify which settings of each IoTdevice/appliance is enabled based on the user's ability baseline. Forexample, the system may modify certain IoT devices that use sound alarmsto instead use blinking lights for a user that has difficulty hearing.For example, with smart lightbulbs and presence tracking, the IoTdevices may use blinking light alarms that can follow a personthroughout the home until deactivated.

In some embodiments, the change in ability baseline may be determinedbased on a predetermined time period. For example, the change in abilitybaseline may be measured during a 24-hour schedule and adjustment ofsettings for various IoT devices may be made accordingly. For example, auser that requires a hearing device during the day may not utilize thedevice while sleeping. The system may recognize the change in the user'sability baseline during the evening hours (e.g., image recognition ofthe user not wearing the hearing device and/or through user notificationthey do not wear the device at certain times during the night) andincrease volume settings for various IoT device (e.g., fire alarms,smart clocks) such that the user may hear the respective devices whennot wearing their hearing device.

Once the adjustments to the settings of the IoT devices are made, theprocess 200 may return to step 215 to monitor the interactions of theone or more users to detect any further changes in baseline ability. Inthis way, the system will make adjustments to the settings of the IoTdevice as changes in ability for the one or more users change over time.

Referring now to FIG. 3, shown is a flow diagram of an example process300 for adjusting the ability baseline of a user based on a cohort, inaccordance with embodiments of the present disclosure. Process 300 maybe in addition to or a subset of process 200. The process 300 may beperformed by processing logic that comprises hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processor), firmware, or a combination thereof. Insome embodiments, the process 300 is a computer-implemented process. Theprocess 300 may be performed by processor 104 exemplified in FIG. 1.

The process 300 begins by collecting historical ability baseline datafor a plurality of users. This is illustrated at step 305. For example,the system may collect user ability baseline data for multiple users(e.g., diversity of participants, various demographics, etc.) acrossmultiple digital environments (e.g., systems having varying IoTdevices).

The process 300 continues by correlating the historical ability baselinedata with adjustments of one or more settings for the plurality of IoTdevices. This is illustrated at step 310. For example, the system, usingmachine learning, may determine what type of setting adjustments weremade for the various types of user abilities to result in successfulinteraction of the users with respective IoT devices. For example, thesystem may determine that most users having a certain vision capabilitycould adequately see size 16 font on their computer screens.

Once the correlation is performed, the process 300 continues by groupingthe plurality of users into one or more cohorts based on similar abilitybaseline data and similar adjustments of the one or more settings of theplurality of IoT devices. This is illustrated at step 315. The process300 continues by determining one or more users fall within the one ormore cohorts. This is illustrated at step 320. Once the appropriatecohort is determined for the one or more users, the process 300continues by adjusting the ability baseline and the one or more settingsof the IoT devices for the one or more users based on the parameters ofthe one or more cohorts. This is illustrated at step 325. For example,the system may group users having similar vision capabilities in aspecific cohort based on successful adjustment of font settings. Oncethe cohort is established, font settings for new users placed in thecohort will automatically be updated accordingly.

Referring now to FIG. 4, shown is a flow diagram of an example process400 for modifying one or more settings of an IoT device using a ruledetermination, in accordance with embodiments of the present disclosure.Process 400 may be in addition to or a subset of process 200. Theprocess 400 may be performed by processing logic that comprises hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processor) firmware, or acombination thereof. In some embodiments, the process 400 is acomputer-implemented process. The process 400 may be performed byprocessor 104 exemplified in FIG. 1.

In embodiments, the process 400 begins by detecting two or more usersare attempting to use at least one IoT device of a plurality of IoTdevices concurrently. This is illustrated at step 405. Formulti-inhabitant spaces (e.g., homes, community centers, etc.) thesystem may determine which users are present in the space and negotiatepreference settings of IoT devices when more than 1 individual isdetected. For example, in a multi-inhabitant space, the system maydetermine which users are presently interacting with an IoT device(e.g., using image recognition through an IoT camera, various trackingIoT sensors, or through other user recognition means appreciated by oneskilled in the art). It is assumed that the two or more users havedifferent ability baseline settings. For example, a first user may bewatching a smart television at a preferred volume (e.g., first abilitybaseline). A second user may enter the same area and prefer to have thevolume level of the smart television at a lower volume (e.g., a secondability baseline) based on user preferences.

The process 400 continues by applying a rule to determine which of thedifferent ability baselines to apply to the IoT device. The isillustrated at step 410. In embodiments, the system may apply any typeof rule for determining which ability baseline to apply to the IoTdevice. For example, the system may detect the multi-inhabitants andenact a preferential voting/ranking rule for applying preferencesettings. For example, the first user may be identified (via userprofile information) as the head of the household, while the second useris identified as a child. In such an instance, the system may determineby applying a rule determination that the first user outranks the seconduser, therefore, the first ability baseline will be applied to the IoTdevice(s).

Once the rule determination has been applied the process 400 continuesby modifying the one or more settings of the IoT device according to therule determination. This is illustrated at step 415. For example,because the first user has been determined to outrank the second userwhen applying the rule, the system will maintain the volume level of thesmart television according to the first user's ability baseline. In someembodiments, the system may combine various ability baselines ofmultiple users such that the settings may accommodate all or the mostusers. For example, the system may lower the volume setting of thetelevision to accommodate the second user while enabling closed captionsfor the first user. In some embodiments, both users may have an abilitybaseline that includes both a preferred volume and an acceptable rangevolume. If the acceptable range volumes of the users overlap, the systemwill adjust the volume within the acceptable range accordingly.

In embodiments, the system may prompt the users before enacting thechange or alternatively may auto-enact any changes. In some embodiments,the rule determination may not be made if the system has determined thesecond user is not using the IoT device. For example, the second usermay have been detected in the same space as the first user but has beendetermined not to be watching the smart television (e.g., second user isdetermined to be reading a book via image recognition). The system maylocalize person/device/appliance, then set accordingly to proximity ifnot affecting other inhabitants (e.g. watching tv alone even thoughother people are detected in home)

Referring now to FIG. 5, shown is a high-level block diagram of anexample computer system 1101 that may be used in implementing one ormore of the methods, tools, and modules, and any related functions,described herein (e.g., using one or more processor circuits or computerprocessors of the computer), in accordance with embodiments of thepresent disclosure. In some embodiments, the major components of thecomputer system 1101 may comprise one or more CPUs 1102, a memorysubsystem 1104, a terminal interface 1112, a storage interface 1116, anI/O (Input/Output) device interface 1114, and a network interface 1118,all of which may be communicatively coupled, directly or indirectly, forinter-component communication via a memory bus 1103, an I/O bus 1108,and an I/O bus interface 1110.

The computer system 1101 may contain one or more general-purposeprogrammable central processing units (CPUs) 1102A, 1102B, 1102C, and1102D, herein generically referred to as the CPU 1102. In someembodiments, the computer system 1101 may contain multiple processorstypical of a relatively large system; however, in other embodiments thecomputer system 1101 may alternatively be a single CPU system. Each CPU1102 may execute instructions stored in the memory subsystem 1104 andmay include one or more levels of on-board cache. In some embodiments, aprocessor can include at least one or more of, a memory controller,and/or storage controller. In some embodiments, the CPU can execute theprocesses included herein (e.g., process 200, 300, and 400).

System memory subsystem 1104 may include computer system readable mediain the form of volatile memory, such as random access memory (RAM) 1122or cache memory 1124. Computer system 1101 may further include otherremovable/non-removable, volatile/non-volatile computer system datastorage media. By way of example only, storage system 1126 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media, such as a “hard drive.” Although not shown, a magneticdisk drive for reading from and writing to a removable, non-volatilemagnetic disk (e.g., a “floppy disk”), or an optical disk drive forreading from or writing to a removable, non-volatile optical disc suchas a CD-ROM, DVD-ROM or other optical media can be provided. Inaddition, memory subsystem 1104 can include flash memory, e.g., a flashmemory stick drive or a flash drive. Memory devices can be connected tomemory bus 1103 by one or more data media interfaces. The memorysubsystem 1104 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of various embodiments.

Although the memory bus 1103 is shown in FIG. 5 as a single busstructure providing a direct communication path among the CPUs 1102, thememory subsystem 1104, and the I/O bus interface 1110, the memory bus1103 may, in some embodiments, include multiple different buses orcommunication paths, which may be arranged in any of various forms, suchas point-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface 1110 and the I/O bus 1108 are shown as single units, thecomputer system 1101 may, in some embodiments, contain multiple I/O businterfaces 1110, multiple I/O buses 1108, or both. Further, whilemultiple I/O interface units are shown, which separate the I/O bus 1108from various communications paths running to the various I/O devices, inother embodiments some or all of the I/O devices may be connecteddirectly to one or more system I/O buses.

In some embodiments, the computer system 1101 may be a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). Further, in someembodiments, the computer system 1101 may be implemented as a desktopcomputer, portable computer, laptop or notebook computer, tabletcomputer, pocket computer, telephone, smart phone, network switches orrouters, or any other appropriate type of electronic device.

It is noted that FIG. 5 is intended to depict the representative majorcomponents of an exemplary computer system 1101. In some embodiments,however, individual components may have greater or lesser complexitythan as represented in FIG. 5, components other than or in addition tothose shown in FIG. 5 may be present, and the number, type, andconfiguration of such components may vary.

One or more programs/utilities 1128, each having at least one set ofprogram modules 1130 may be stored in memory subsystem 1104. Theprograms/utilities 1128 may include a hypervisor (also referred to as avirtual machine monitor), one or more operating systems, one or moreapplication programs, other program modules, and program data. Each ofthe operating systems, one or more application programs, other programmodules, and program data or some combination thereof, may include animplementation of a networking environment.

Programs/utilities 1128 and/or program modules 1130 generally performthe functions or methodologies of various embodiments.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and mobile desktops 96.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereinmay be performed in alternative orders or may not be performed at all;furthermore, multiple operations may occur at the same time or as aninternal part of a larger process.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

What is claimed is:
 1. A computer-implemented method comprising:detecting a plurality of IoT devices, wherein the plurality of IoTdevices are associated with one or more users; determining an abilitybaseline for the one or more users when interacting with the pluralityof IoT devices, wherein determining the ability baseline comprises:measuring a response metric of the one or more users when interactingwith one or more IoT devices of the plurality of IoT devices; andanalyzing a set of answers from the one or more users in response to aquestionnaire, wherein a first answer includes an indication that theone or more users uses a compensatory device, wherein the abilitybaseline is modified based on a type of the compensatory device;monitoring interactions of the one or more users with the plurality ofIoT devices; detecting a change in the ability baseline for the one ormore users when interacting with at least one IoT device of theplurality of IoT devices; adjusting, in response to detecting the changein the ability baseline for the one or more users, one or more settingsrelated to the at least one IoT device; and propagating the adjustmentto the one or more settings of the at least one IoT device to the otherIoT devices of the plurality of IoT devices.
 2. The computer-implementedmethod of claim 1, wherein the ability baseline for the one or moreusers is further determined, in part, by simulating the one or moresettings in a feedback loop presented to the one or more users.
 3. Thecomputer-implemented method of claim 1, wherein the ability baseline isfurther determined, in part, by measuring game play ability of the oneor more users using gamification.
 4. The computer-implemented method ofclaim 1, wherein adjusting the one or more settings related to the atleast one IoT device further comprises: correlating user interfacesettings of the at least one IoT device to the detected change in theability baseline for the one or more users; and modifying, based on thecorrelating, the user interface settings of the at least one IoT device.5. The computer-implemented method of claim 1, further comprising:collecting historical ability baseline data for a plurality of users;correlating the historical ability baseline data with adjustments of oneor more settings for the plurality of IoT devices; and grouping theplurality of users into one or more cohorts based on similar abilitybaseline data and similar adjustments of the one or more settings of theplurality of IoT devices.
 6. The computer-implemented method of claim 5,further comprising: determining the one or more users fall within theone or more cohorts; and adjusting the ability baseline and the one ormore settings based on the one or more cohorts.
 7. Thecomputer-implemented method of claim 6, further comprising: detectingtwo or more users are attempting to use the at least one IoT device ofthe plurality of IoT devices concurrently, wherein the two or more usershave different ability baselines; applying a rule to determine which ofthe different ability baselines to apply to the at least one IoT device;and modifying the one or more settings of the at least one IoT deviceaccording to the rule determination.
 8. A system comprising: aprocessor; and a computer-readable storage medium communicativelycoupled to the processor and storing program instructions which, whenexecuted by the processor, cause the processor to perform a methodcomprising: determining an ability baseline for a user, whereindetermining the ability baseline comprises: measuring a response metricof the user when interacting with the system; and analyzing a set ofanswers from the user in response to a questionnaire, wherein a firstanswer includes an indication that the user uses a compensatory device,wherein the ability baseline is modified based on a type of thecompensatory device; monitoring interactions of the user with thesystem; detecting a change in the ability baseline for the user based onthe monitoring; adjusting, in response to detecting the change in theability baseline, one or more settings related to the system; andtransmitting the adjustment to the one or more settings to at least oneIoT device associated with the user.
 9. The system of claim 8, whereinthe ability baseline is further determined, in part, by measuring gameplay ability of the user using gamification.
 10. The system of claim 8,wherein adjusting the one or more settings related to the system furthercomprises: correlating user interface settings of the system to thedetected change in the ability baseline for the user; and modifying,based on the correlating, the user interface settings of the system. 11.The system of claim 8, wherein the method performed by the processorfurther comprises: collecting historical ability baseline data for aplurality of users; correlating the historical ability baseline datawith adjustments of one or more settings related to the system; andgrouping the plurality of users into one or more cohorts based onsimilar ability baseline data and similar adjustments of the one or moresettings.
 12. The system of claim 11, wherein the method performed bythe processor further comprises: determining the user falls within theone or more cohorts; and adjusting the ability baseline and the one ormore settings based on the one or more cohorts.
 13. The system of claim8, wherein the method performed by the processor further comprises:detecting two or more users are attempting to use the systemconcurrently, wherein the two or more users have different abilitybaselines; applying a rule to determine which of the different abilitybaselines to apply to the system; and modifying the one or more settingsof the system according to the rule determination.
 14. A computerprogram product comprising a computer-readable storage medium havingprogram instructions embodied therewith, wherein the computer-readablestorage medium is not a transitory signal per se, the programinstructions executable by a processor to cause the processor to performa method comprising: detecting a plurality of IoT devices, wherein theplurality of IoT devices are associated with one or more users;determining an ability baseline for the one or more users wheninteracting with the plurality of IoT devices, wherein determining theability baseline comprises: measuring a response metric of the one ormore users when interacting with one or more IoT devices of theplurality of IoT devices; and analyzing a set of answers from the one ormore users in response to a survey, wherein a first answer includes anindication that the one or more users uses a compensatory device,wherein the ability baseline is modified based on a type of thecompensatory device; monitoring interactions of the one or more userswith the plurality of IoT devices; detecting a change in the abilitybaseline for the one or more users when interacting with at least oneIoT device of the plurality of IoT devices; adjusting, in response todetecting the change in the ability baseline for the one or more users,one or more settings related to the at least one IoT device; andpropagating the adjustment to the one or more settings of the at leastone IoT device to the other IoT devices of the plurality of IoT devices.15. The computer program product of claim 14, wherein the abilitybaseline for the one or more users is further determined, in part, bysimulating the one or more settings in a feedback loop presented to theone or more users.
 16. The computer program product of claim 14, whereinthe method performed by the processor further comprises: collectinghistorical ability baseline data for a plurality of users; correlatingthe historical ability baseline data with adjustments of one or moresettings for the plurality of IoT devices; grouping the plurality ofusers into one or more cohorts based on similar ability baseline dataand similar adjustments of the one or more settings of the plurality ofIoT devices; determining the one or more users fall within the one ormore cohorts; and adjusting the ability baseline and the one or moresettings based on the one or more cohorts.
 17. The computer programproduct of claim 14, wherein the method performed by the processorfurther comprises: detecting two or more users are attempting to use theat least one IoT device of the plurality of IoT devices concurrently,wherein the two or more users have different ability baselines; applyinga rule to determine which of the different ability baselines to apply tothe at least one IoT device; and modifying the one or more settings ofthe at least one IoT device according to the rule determination.
 18. Thecomputer-implemented method of claim 1, wherein measuring the responsemetric comprises measuring an average time that the one or more userstake to respond to an audible alarm.